Integration of wind power with the grid has become an important problem. For integration, a producer needs to bid in a time-ahead market to deliver an amount of energy at a future point in time. Because wind speed and price are both uncertain, a producer needs to place bids on the basis of expected wind power yield and price. To this end, improving the accuracy of the prediction of wind speed has received much attention. However, the trade-off between expected profit and the prediction errors over a multi-period setting has been less studied. We fill this gap by quantifying trade-offs between profits and prediction errors. First, we obtain, under idealized conditions on the price and the yield processes, an optimal bid strategy as a closed-form expression. Next, we evaluate the profit-vs-prediction trade-off using this idealized bidding strategy on synthetic datasets which satisfy all the idealistic assumptions. We also consider two baselines - a naive strategy and an oracle strategy that has perfect knowledge over a limited horizon. Finally, we relax our assumptions and evaluate all strategies under real-world datasets. We identify and work around limitations of the idealized bidding strategy when the underlying assumptions are violated. On synthetic datasets, with no buffering and a (relative) prediction error of 25% , we find that our bidding approach performs significantly better than a naive approach and compares favourably (86%) to an oracle with a look-ahead of two time-slots and infinite buffer. On real-world datasets, with buffer equivalent to 20% of the maximum yield, our approach exceeds the naive approach by 25%, while remaining within 62% of a two-step look-ahead oracle that uses infinite buffering.